Artificial Intelligence
February 13, 2025
Make us preferred on Google
Read time:9 minsUpdated:June 12, 2024
An AI agent is software that takes a goal, decides the steps to reach it, and carries them out using tools such as APIs, databases and search, checking its results as it goes. An agentic workflow is a business process in which one or more AI agents do this work, with checks and human approval built in.
An AI agent is a software system that perceives its environment, decides what to do, and acts to reach a goal set by a person. Today most AI agents use a large language model to understand requests and plan, and they use tools, such as a CRM, email or a database, to take real actions. Example: a support agent that reads a refund request, checks the order, issues the refund within policy and replies to the customer.
Unlike earlier software that only follows fixed rules, an AI agent chooses the best next action itself, within the limits you set.
Every agent runs the same loop: perceive (read the input), plan (decide the next step), act (call a tool), observe (check the result), and repeat until the goal is met or it needs a person.
An agentic workflow is a business process where AI agents plan and carry out the steps, use tools and data, and hand over to people at set points. Unlike fixed automation, the agent decides the next step at run time, so it can handle messy inputs such as emails, documents and calls.
The agent is the worker; the workflow is the process. One workflow can use several agents, for example one to read documents, one to check rules and one to draft a reply. See real examples in our guide to agentic AI workflow use cases.
Business use of AI changed in November 2022 with the launch of ChatGPT, an AI assistant that could hold human-like conversations, write and answer questions. GPT models were versatile, but they could not act: they had no access to live data or tools.
This led to AI agents: systems that combine a language model with tools so they can complete tasks, not only answer. Since 2024, major vendors have released agent products and tools, such as Salesforce Agentforce, OpenAI's agent tools and Anthropic's Model Context Protocol for connecting agents to data.
These five classic types come from Russell and Norvig's textbook Artificial Intelligence: A Modern Approach. In multi-agent business systems you also see orchestrator agents, which plan and assign work, and worker agents, which execute the tasks.
In business, AI agents now handle work such as customer service, where they decide whether to resolve an issue or escalate it to a person, operations monitoring and research. They learn and adapt their approach based on the results of their actions.
Businesses can get more from AI agents by implementing Agentic workflows into business operations. Agentic workflows with automation enhance operational efficiency and contribute significantly to the overall strategic advancement of the business.
Here are all the key impacts created on traditional business processes and operations by agentic workflows.
Every agentic workflow is built from the same core parts:
The agentic workflow’s structure and function in the business setting is to maximize the efficiency and effectiveness of the business operations through integrating AI agents. The structure and function of agentic workflows include
Agentic workflows are used across many sectors. Here are the main ones.
Agents help with admin work such as clinical note drafts, appointment scheduling, record keeping and insurance claims. Clinical decisions stay with doctors.
Agents handle routine questions end to end, use past interactions to understand context, and pass complex cases to a person with a summary. Personal suggestions based on purchase history also lift satisfaction and sales.
In fintech, agents process large volumes of market and transaction data, prepare insights for analysts, flag unusual patterns that may signal fraud, and help review documents for compliance. People approve trades and payments.
Agents watch stock, orders and shipping updates, forecast demand, suggest new routes or suppliers when something goes wrong, and draft orders for a planner to approve.
Agents build personal lesson plans based on each student's pace, strengths and weaknesses, spot gaps in knowledge, suggest exercises, and take over admin tasks such as course registration.
Agents help assign tasks based on skills and workload, flag delays early, forecast resource needs and suggest training where skills are missing.
What’s the cost of not understanding AI agents in 2026?
AI agents are moving from pilots into daily operations. These are the main directions:
An AI agent plans and acts toward a goal using tools; an agentic workflow is the business process built around one or more agents, with checks and human approval. Start with one workflow that has clear milestones and measurable results.
Codiste is an AI agent development studio that builds AI agents and agentic workflows for funded founders and CTOs. Talk to us about your first agent.
CTO & Co-Founder | Codiste
Nishant builds where ambitious ideas meet production reality. As CTO & Co-Founder at Codiste, he has helped ship 150+ AI systems. Through The CTO Story, he gets technical leaders talking about what rarely makes the slide deck: the trade-offs, failures, and decisions behind what actually ships.
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